Metadata-Version: 2.1
Name: meshzoo
Version: 0.9.4
Summary: Collection of explicitly constructed meshes
Author-email: Nico Schlömer <nico.schloemer@gmail.com>
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Dist: numpy >= 1.20.0
Requires-Dist: matplotlib ; extra == "all"
Requires-Dist: matplotlib ; extra == "plot"
Project-URL: Code, https://github.com/nschloe/meshzoo
Project-URL: Funding, https://github.com/sponsors/nschloe
Project-URL: Issues, https://github.com/nschloe/meshzoo/issues
Provides-Extra: all
Provides-Extra: plot

# meshzoo

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When generating meshes for FEM/FVM computations, sometimes your geometry is so simple
that you don't need a complex mesh generator (like
[pygmsh](https://github.com/nschloe/pygmsh/),
[MeshPy](https://github.com/inducer/meshpy),
[mshr](https://bitbucket.org/fenics-project/mshr),
[pygalmesh](https://github.com/nschloe/pygalmesh/),
[dmsh](https://github.com/nschloe/dmsh/)),
but something simple and fast that makes use of the structure of the domain. Enter
meshzoo.

### Examples

#### Triangle

<img src="https://nschloe.github.io/meshzoo/triangle.svg" width="20%">

```python
import meshzoo

bary, cells = meshzoo.triangle(8)

# corners = numpy.array(
#     [
#         [0.0, -0.5 * numpy.sqrt(3.0), +0.5 * numpy.sqrt(3.0)],
#         [1.0, -0.5, -0.5],
#     ]
# )
# points = numpy.dot(corners, bary).T

# Process the mesh, e.g., write it to a file using meshio
# meshio.write_points_cells("triangle.vtk", points, {"triangle": cells})
```

#### Rectangle

<table width="100%">
  <tr width="100%">
  <td width="50%"><img src="https://nschloe.github.io/meshzoo/rectangle.svg"/></td>
  <td width="50%"><img src="https://nschloe.github.io/meshzoo/rectangle-quad.svg"/></td>
  </tr>
</table>

```python
import meshzoo
import numpy as np

points, cells = meshzoo.rectangle_tri(
    np.linspace(0.0, 1.0, 11),
    np.linspace(0.0, 1.0, 11),
    variant="zigzag",  # or "up", "down", "center"
)

points, cells = meshzoo.rectangle_quad(
    np.linspace(0.0, 1.0, 11),
    np.linspace(0.0, 1.0, 11),
    cell_type="quad4",  # or "quad8", "quad9"
)
```

#### Regular polygon

| <img src="https://nschloe.github.io/meshzoo/4gon.svg" width="70%"> | <img src="https://nschloe.github.io/meshzoo/6gon.svg" width="70%"> | <img src="https://nschloe.github.io/meshzoo/9gon.svg" width="70%"> |
| :----------------------------------------------------------------: | :----------------------------------------------------------------: | :----------------------------------------------------------------: |
|                        `meshzoo.ngon(4, 8)`                        |                        `meshzoo.ngon(6, 8)`                        |                        `meshzoo.ngon(9, 8)`                        |

```python
import meshzoo

points, cells = meshzoo.ngon(5, 11)
```

#### Disk

| <img src="https://nschloe.github.io/meshzoo/4gon_disk.svg" width="70%"> | <img src="https://nschloe.github.io/meshzoo/6gon_disk.svg" width="70%"> | <img src="https://nschloe.github.io/meshzoo/9gon_disk.svg" width="70%"> |
| :---------------------------------------------------------------------: | :---------------------------------------------------------------------: | :---------------------------------------------------------------------: |
|                          `meshzoo.disk(4, 8)`                           |                          `meshzoo.disk(6, 8)`                           |                          `meshzoo.disk(9, 8)`                           |

The disk meshes are inflations of regular polygons.

```python
import meshzoo

points, cells = meshzoo.disk(6, 11)

points, cells = meshzoo.disk_quad(10, cell_type="quad4")  # or "quad8", "quad9"
```

#### Möbius strip

<img src="https://nschloe.github.io/meshzoo/moebius.png" width="20%">

```python
import meshzoo

points, cells = meshzoo.moebius(num_twists=1, nl=60, nw=11)
```

#### Sphere (surface)

| <img src="https://nschloe.github.io/meshzoo/uv_sphere.png" width="80%"> | <img src="https://nschloe.github.io/meshzoo/geo-sphere.png" width="60%"> |
| :---------------------------------------------------------------------: | :----------------------------------------------------------------------: |

```python
import meshzoo

points, cells = meshzoo.uv_sphere(num_points_per_circle=20, num_circles=10, radius=1.0)
points, tri, quad = meshzoo.geo_sphere(
    num_points_per_circle=20, num_circles=10, radius=1.0
)
```

Spheres can also be generated by refining the faces of [platonic
solids](https://en.wikipedia.org/wiki/Platonic_solid) and then "inflating" them. meshzoo
implements a few of them. The sphere generated from the icosahedron has the
highest-quality (most equilateral) triangles.

All cells are oriented such that its normals point outwards.

| <img src="https://nschloe.github.io/meshzoo/tetra-sphere.png" width="70%"> | <img src="https://nschloe.github.io/meshzoo/octa-sphere.png" width="70%"> | <img src="https://nschloe.github.io/meshzoo/icosa-sphere.png" width="70%"> |
| :------------------------------------------------------------------------: | :-----------------------------------------------------------------------: | :------------------------------------------------------------------------: |
|                         `meshzoo.tetra_sphere(10)`                         |                         `meshzoo.octa_sphere(10)`                         |                         `meshzoo.icosa_sphere(10)`                         |

#### Ball (solid)

| <img src="https://nschloe.github.io/meshzoo/ball-tetra.png" width="70%"> | <img src="https://nschloe.github.io/meshzoo/ball-hexa.png" width="70%"> |
| :----------------------------------------------------------------------: | :---------------------------------------------------------------------: |

```python
import meshzoo

points, cells = meshzoo.ball_tetra(10)
points, cells = meshzoo.ball_hexa(10)
```

#### Tube

<img src="https://nschloe.github.io/meshzoo/tube.png" width="20%">

```python
import meshzoo

points, cells = meshzoo.tube(length=1.0, radius=1.0, n=30)
```

#### Cube

| <img src="https://nschloe.github.io/meshzoo/cube.png" width="70%"> | <img src="https://nschloe.github.io/meshzoo/cube-hexa.png" width="50%"> |
| :----------------------------------------------------------------: | :---------------------------------------------------------------------: |

```python
import meshzoo
import numpy as np

points, cells = meshzoo.cube_tetra(
    np.linspace(0.0, 1.0, 11), np.linspace(0.0, 1.0, 11), np.linspace(0.0, 1.0, 11)
)
points, cells = meshzoo.cube_hexa(
    np.linspace(0.0, 1.0, 11), np.linspace(0.0, 1.0, 11), np.linspace(0.0, 1.0, 11)
)
```

### Extra, extra

In addition to this, the
[`examples/`](https://github.com/nschloe/meshzoo/tree/main/examples) directory
contains a couple of instructive examples for other mesh generators.

### Installation

meshzoo is [available from the Python Package
Index](https://pypi.org/project/meshzoo/), so simply do

```
pip install meshzoo
```

to install.

### Testing

To run the meshzoo unit tests, check out this repository and run

```
pytest
```

### License

This software is published under the [GPLv3
license](https://www.gnu.org/licenses/gpl-3.0.en.html).

